Excerpt from course description

Customer and Market Analytics


Are you using the data your business has to your best advantage? This course teaches you (without lots of math and statistics) how to understand and employ analytics to improve your business results.

Should you blindly trust analytic reports served to you by analysts or statistical software? Be careful if you do!

In the fast-paced market conditions, where competition is fierce, rate of technological change wild and consumers empowered and unpredictable, it has become more crucial than ever to understand the trade-offs between elements that are driving business performance. While data availability is overwhelming, managers are struggling in analyzing the increasing amount of information they have. Never before did managers have this much information on customers, partners and competition at disposal to make informed, smarter decisions; yet they are more than ever criticized about the lack of actionable insights that derive from it. McKinsey Consulting predicted that the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use data to make effective business decisions. Even for managers who have IT specialists and computer scientists in their team, the understanding of how to avoid “garbage in-garbage out” situations is essential. Practice have shown that business/strategic decisions cannot be left to technologies alone because competitive advantage is still in the decisions that managers make about their investments in customers and markets. The quest to find sustainable advantages to satisfy customers better than competition requires an understanding of how different aspects of marketing investment can be tight together, how to evaluate the current potential and future contribution of each customer and how to reap the advantage from customer relationships and social media investments.

 In this course you learn how to increase the effectiveness of strategic decisions. We use an “Explain-Show-Do-Practice” approach to teaching that encompasses explanations in the lectures followed by a combination of class discussion, case study analysis and practical hands-on exercises in Excel (cloud-based solution). We do not go deeply into the statistics and mathematics behind the methods used in the academic models behind the tools. In this course, we always start from an identification of a strategic business problem (e.g. “Should I invest in option A or option B”). We will then summarize what we know from years of practice and research on what works and what does not. We follow up these insights with an understanding of what data would I need to solve the problem and intuitively how a model/analytics work to solve this issue. We teach you hands-on, based on actual case studies and their datasets how could one analyze the data to get insights. We use a cloud-based solution Enginius (that can use Excel spreadsheets) to make this course highly relevant and applicable to manager’s actual decision-making. Strategically, we then focus our attention on how to evaluate the output that you would get from the software or analytics experts in the firm to make more effective business decisions.


  • To those reading analytic reports:
  1. Executives in leadership positions who want to understand how analytics could be used to improve business performance rather than blindly trust into outputs of analysts
  2. For business development professionals and management consultants who need to understand how analytics can create value for their clients
  • To those who do analytics:
  1. for product, brand and marketing managers who need to understand how to better leverage data and run analytic models in intuitive, simple way
  2. professionals who already have background in analytics or computer science, but whose roles and projects are becoming increasingly strategic, so they need to develop further strategic skills to bridge the gap between analytics and business strategy.

Our classroom experience shows it is beneficial to team up the two groups of executives above to teach them how to bridge the gap between analytics and strategy. Therefore, the course can both to managers without extensive marketing background as well as to those who work on marketing issues on a daily base. The highest impact of learning is achieved when concepts are discussed and practiced across the whole company in interdepartmental teams consisting of managers with diverse background: analytics, finance, accounting, operations and marketing, and the class would benefit from interactions and contributions from different backgrounds.

This course would be suited for students who have basic skills and knowledge in business administration. No advanced understanding of mathematics or econometrics is necessary for this course. Basic understanding of statistics (at an undergraduate business studies level) is beneficial for following the course, although some basic concepts that we need will be explained in class and in online materials and exercises. Basic knowledge and use of Microsoft Excel program is preferable, because most exercises are linked to a managerially oriented cloud-solution Enginius. Advanced data science and computer programing skills are not needed for this course (albeit we provide insights for more advanced students in supplemental materials which are linked to programming in R and machine learning algorithms). 

Course content

Topics and potential business problems that could be addressed:


What data you put in

What you get

Analytical Tool


  • Better understand the market I serve and my customers.
  • Identify different segments in a market.
  • Choose attractive customer segments for targeting its marketing programs.
  • Profile my customers


  • Customers' importance ratings for each measure of value for offerings in a product class
  • Customer descriptors (demographic or firmographic variables)
  • Number, size, and profile of needs-based market segments
  • Identification of factors that differentiate segments, both in terms of needs and descriptors
  • Classification tool to allocate any potential customer to a segment based on customer descriptors.

Segmentation, & Targeting Analysis


Cluster analysis

Discriminant analysis

Units 2-3-4

  • Understand how customers view  product(s) relative to competitive products
  • Customers' rating of focal brand and key competitors on dimensions of merit
  • Individual customer preference ratings of all competitors
  • Perceptual map, showing which brands are closest to one another.
  • Attributes that differentiate brands
  • Locations of individual customer preferences
  • Projected market share associated with current and new positions on the map

Positioning and perceptual mapping

Units 2-3-4

  • Measure, analyze, and predict customers' responses to new products and to new features of existing products (e.g. which price to charge)
  • Design new products that maximize customer utility.
  • Forecast sales/market share of alternative product bundles.
  • Identify market segments for which a given product concept has high value.
  • Identify the "best" product concept for a target segment.
  • Customer ratings of a set of real or potential product offerings, defined by their key attributes
  • Market share of existing products
  • New product profiles


  • Customers' preferences and responses to new products
  • Relative worth of product attributes
  • Optimal product design
  • Market share estimates for alternative products
  • Customers' willingness to pay for product attributes
  • Potential incremental revenue from new offerings/features

Conjoint analysis


Units 5-6-7-8

  • Analyze and explain the choices individual customers make in the market.
  • Understand which elements drive customer decision to buy your product or not?
  • Estimate customer’s willingness to buy at different price points (and to interpolate between these price points).
  • Customer's choice data for alternative offerings
  • Customer ratings of alternative offerings on their key attributes
  • Buying intention for a product at a number of different price points
  • Purchase probabilities, predicted and observed choices of customers
  • Factors influencing customer choice, including brand as well as performance attributes
  • Aggregate demand level estimate for any price level

Customer choice model


Units 5-6-7-8

  • Calculate customer's value to the organization over the entire history of the relationship
  • Understand who are my most valuable customers
  • Observed churn rates
  • Customer acquisition cost
  • Number of customers/segments
  • Gross margins by segment
  • Customer transition probabilities across segments
  • Value of current customer base
  • Time required to recoup customer investments
  • ROI on customer/segment investments
  • Size and profitability of customer segments over time; sensitivity to marketing investment plan

Customer Lifetime Value Analysis

Units 9-10-11

  • Optimize resource sizing and resource allocations across segments, products, channels, etc.
  • How much should we spend in total during a given planning horizon?
  • How should that spending get allocated to each product or market segment? To each marketing mix element? How much of our budget should be spent on advertising and other forms of impersonal marketing communications? On sales promotions? On the sales force?
  • How should budgets given to an individual (e.g., salesperson, manager of department) be allocated? To customers? To geographies? To sub-elements of the marketing communications mix? Over time?


  • Analyzing what customers say and feel about your brands on social media
  • Number of market segments, products, geographies or other basis for resource allocation
  • Current level of spending and associated sales
  • Profit margins
  • Response functions - how sales would change if spending were higher or lower than current spending
  • Constraints (minimum / maximum) for each basis unit


  • Optimal level of total spending (across media spending)
  • Optimal allocation of spending across units
  • Profit associated with optimal plan versus current plan
  • Incremental gain or loss associated with changes from current or optimal plan
  • Across media spending (different channels)


Resource allocation analysis











Sentiment analysis and ROI on social media

Units 9-10-11


This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.